Recent advancements in deep learning techniques have pushed forward the frontiers of real photograph denoising. However, due to the inherent pooling operations in the spatial domain, current CNN-based denoisers are biased towards focusing on low-frequency representations, while discarding the high-frequency components. This will induce a problem for suboptimal visual quality as the image denoising tasks target completely eliminating the complex noises and recovering all fine-scale and salient information. In this work, we tackle this challenge from the frequency perspective and present a new solution pipeline, coined as frequency attention denoising network (FADNet). Our key idea is to build a learning-based frequency attention framework, where the feature correlations on a broader frequency spectrum can be fully characterized, thus enhancing the representational power of the network across multiple frequency channels. Based on this, we design a cascade of adaptive instance residual modules (AIRMs). In each AIRM, we first transform the spatial-domain features into the frequency space. Then, a learning-based frequency attention framework is devised to explore the feature inter-dependencies converted in the frequency domain. Besides this, we introduce an adaptive layer by leveraging the guidance of the estimated noise map and intermediate features to meet the challenges of model generalization in the noise discrepancy. The effectiveness of our method is demonstrated on several real camera benchmark datasets, with superior denoising performance, generalization capability, and efficiency versus the state-of-the-art.

中文翻译：

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学习频域注意力以实现灵活的真实照片去噪


深度学习技术的最新进展推动了真实照片去噪的前沿。然而，由于空间域中固有的池化操作，当前基于 CNN 的降噪器偏向于关注低频表示，而丢弃高频分量。这将导致视觉质量不佳的问题，因为图像去噪任务的目标是完全消除复杂的噪声并恢复所有精细尺度和显着信息。在这项工作中，我们从频率角度应对这一挑战，并提出了一种新的解决方案管道，称为频率注意去噪网络（FADNet）。我们的关键思想是建立一个基于学习的频率注意力框架，可以充分表征更广泛频谱上的特征相关性，从而增强网络在多个频道上的表征能力。基于此，我们设计了一系列自适应实例残差模块（AIRM）。在每个 AIRM 中，我们首先将空间域特征转换到频率空间。然后，设计了一个基于学习的频率注意框架来探索频域中转换的特征相互依赖性。除此之外，我们通过利用估计噪声图和中间特征的指导引入自适应层，以应对噪声差异中模型泛化的挑战。我们的方法的有效性在几个真实相机基准数据集上得到了证明，与最先进的技术相比，具有卓越的去噪性能、泛化能力和效率。